What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Mechanical engineering
- Mathematical optimization
Liang Gao focuses on Mathematical optimization, Job shop scheduling, Flow shop scheduling, Algorithm and Metaheuristic.
His study in Mathematical optimization is interdisciplinary in nature, drawing from both Scheduling, Dynamic priority scheduling and Benchmark.
His study looks at the relationship between Job shop scheduling and fields such as Genetic algorithm, as well as how they intersect with chemical problems.
His Flow shop scheduling study which covers Hybrid algorithm that intersects with Automated planning and scheduling.
His work deals with themes such as Failure probability, Design of experiments, Kriging, Upper and lower bounds and Robustness, which intersect with Algorithm.
In his work, Machining is strongly intertwined with Optimization problem, which is a subfield of Metaheuristic.
His most cited work include:
- A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method (431 citations)
- An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem (349 citations)
- A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis (274 citations)
What are the main themes of his work throughout his whole career to date?
Liang Gao mostly deals with Mathematical optimization, Algorithm, Job shop scheduling, Artificial intelligence and Scheduling.
His Mathematical optimization study integrates concerns from other disciplines, such as Scheduling and Flow shop scheduling.
His Flow shop scheduling study results in a more complete grasp of Dynamic priority scheduling.
As part of the same scientific family, Liang Gao usually focuses on Algorithm, concentrating on Benchmark and intersecting with Differential evolution.
His Job shop scheduling research is multidisciplinary, incorporating elements of Energy consumption, Efficient energy use and Metaheuristic.
His studies in Artificial intelligence integrate themes in fields like Fault, Machine learning and Pattern recognition.
He most often published in these fields:
- Mathematical optimization (40.80%)
- Algorithm (14.84%)
- Job shop scheduling (14.42%)
What were the highlights of his more recent work (between 2019-2021)?
- Mathematical optimization (40.80%)
- Artificial intelligence (11.26%)
- Job shop scheduling (14.42%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Mathematical optimization, Artificial intelligence, Job shop scheduling, Topology optimization and Algorithm.
Liang Gao studied Mathematical optimization and Kriging that intersect with Active learning.
His Artificial intelligence research includes themes of Fault, Machine learning and Pattern recognition.
His Job shop scheduling research incorporates themes from Energy consumption, Scheduling, Metaheuristic and Heuristic.
Liang Gao has researched Topology optimization in several fields, including Topology, Interpolation, Topology and Homogenization.
Liang Gao interconnects Optimization problem and Benchmark in the investigation of issues within Evolutionary algorithm.
Between 2019 and 2021, his most popular works were:
- A transfer convolutional neural network for fault diagnosis based on ResNet-50 (50 citations)
- A system active learning Kriging method for system reliability-based design optimization with a multiple response model (44 citations)
- Real-time estimation error-guided active learning Kriging method for time-dependent reliability analysis (38 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Mechanical engineering
- Mathematical optimization
Mathematical optimization, Topology optimization, Algorithm, Job shop scheduling and Artificial intelligence are his primary areas of study.
His research on Mathematical optimization frequently connects to adjacent areas such as Interval.
His Algorithm research includes elements of Swarm behaviour, Mode, Surrogate model and Confidence interval.
His Job shop scheduling study incorporates themes from Linear programming and Energy consumption.
His Artificial intelligence research incorporates elements of Machine learning and Pattern recognition.
The various areas that Liang Gao examines in his Flow shop scheduling study include Scheduling and Efficient energy use.
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